When an information retrieval system, such as a web search engine, returns a list of search results, the list of results typically includes a brief summary of the content of each of the results so that the user can decide whether or not to select and read the full content of a particular result. Traditionally, there have been two ways to construct these summaries.
One way to construct a summary involves using a pre-generated abstract that describes the main topics of the document. With technical articles, these abstracts are usually provided by the authors and are often expressly labeled, within the articles, as abstracts. With news articles, the first paragraph of the article is often used as a summary of the whole article. Such an abstract is sometimes called a “static” abstract because, for each set of search results in which the abstract appears, the contents of the abstract remain the same regardless of the submitted query terms (i.e., user-submitted words and/or phrases).
Another way to construct a summary involves selecting, for inclusion within the summary, a part of the result document text in which the user's query terms (also known as “key words”) appear. Such a summary is sometimes called a “contextual” or “dynamic” abstract because the contents of the summary for a particular document may differ based on the submitted query terms.
More recently, these two approaches have been combined. Some web search engines generate and display search result summaries that may include, within each summary, both (a) snippets of result document text that contain the query terms in context (i.e., excerpts from a dynamic abstract), and (b) brief excerpts or descriptions of the document as a whole (i.e., excerpts from a static abstract). Summaries that include both excerpts from a static abstract and excerpts from a dynamic abstract are sometimes called “smart abstracts.”
There are many instances where even the “smart abstract” approach alone does not provide enough information to a user. The information-seeking process is iterative. Users' information needs often evolve during the search process. Furthermore, different users do not necessarily use the same vocabulary to describe the content for which they are looking. There are often circumstances in which a user would like to learn more about a search result before deciding whether it is worthwhile to click on that result and read the entire contents of the document to which that result corresponds.
Such circumstances frequently occur when the user is accessing a search engine via a small portable device such as mobile phone. The display screen on most mobile phones is so small, and the bandwidth offered by most mobile phones is so narrow, that downloading an entire document and attempting to display that entire document on the display screen is both tedious and expensive in terms of time.
Another problem with existing search result summaries is that they are generated based on a “one size fits all” paradigm. When two different users issue the same query to a search engine, the summaries generated and shown to both users are identical, even though those two users might have completely different intents. For example, two different users might enter the query “digital camera.” One user might be shopping, while the other user might want to learn how digital cameras work. The information that the first user would find valuable in a summary for a particular search result likely will be quite different from the information that the second user would find valuable in a summary for that particular search result. What is needed is a solution to the foregoing problems.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.